The TWIML AI Podcast with Sam Charrington

“What's your take on the [current industry debate, e.g., 'RAG is dead'] argument?”

Often used as an opener to frame the conversation around a timely, sometimes controversial topic.

“Is the enterprise landscape as grim as I imagine regarding [a specific challenge]?”

A way to candidly ask about the maturity and readiness of the market for the guest's technology.

“What was your background before joining this company?”

He asks this early to understand the guest's expertise and the path that led them to their current role.

“Did your observations about [a specific problem, e.g., 'enterprise risk'] lead you to focus on this?”

Connects the guest's personal insights to the origin story of their product or research focus.

“Can you describe the data landscape you have to deal with?”

This question surfaces the foundational technical challenges a guest faces before diving into the solution.

“Can you detail the pipeline, from ingestion to [specific step]?”

A go-to question for getting a granular, step-by-step breakdown of the guest's technology stack.

“How do your users use [product/system]?”

This shifts the focus from technical implementation to the practical user workflow and experience.

“Does the idea of [existing paradigm, e.g., 'Zero Trust'] extend to AI agents?”

This question connects new AI challenges to established concepts in security or IT.

“Where does the enforcement happen and what is the form factor of your system?”

He asks this to understand the practical, physical, or logical implementation of a guest's solution.

“If you need AI to secure AI, what is your approach for injecting AI into the landscape?”

This question explores the meta-level challenge when the solution requires the same technology it's meant to manage.

“Is the work your human experts do essentially [familiar ML term, e.g., 'data labeling']?”

He uses this technique to translate a guest's domain-specific process into more common machine learning concepts.

“Can you detail the [technical aspect, e.g., 'dense vs. sparse'] parts of your retrieval?”

A follow-up used to probe deeper into a specific architectural choice mentioned by the guest.